As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.

翻译：随着对深层学习解决方案的要求增加,解释的必要性就更加重要了。在这一背景下,特别注意了直观化技术,这些技术试图将正确的相关性与网络产出的每个输入像素联系起来。在本文中,我们侧重于分类激活绘图方法,这些方法通过对启动地图的加权平均值提供有效的直观化。为了改进评估和这些方法的可复制性,我们提出了一套新的衡量标准,以量化解释地图,显示更好的效果并简化方法之间的比较。为了评估提案的适宜性,我们比较了整个图像网络验证集的不同基于 CAM 的可视化方法,促进适当的比较和可复制性。